Volume 1, Issue 2

In this article we will discuss the Fast Marching Method which was introduced by James A. Sethian to solve some types of nonlinear partial differential equations efficiently. We will show that this method yields the unique solution to an upwind discretization. Furthermore we will present the correct algorithm for the second order case where existence and unicity of the solution will be proven as well.

Effective validation of distributed real-time and embedded (DRE) system quality-of-service (QoS) properties (e.g., event prioritization, latency, and throughput) requires testing system capabilities in representative execution environments. Unfortunately, evaluating the correctness of such tests is hard since it requires validating many states dispersed across many hardware and software components. To address this problem, this article presents a method called Test Execution (TE) Score for validating execution correctness of DRE system tests and empirically evaluates TE Score in the context of a representative DRE system. Results from this evaluation show that TE Score can determine the percentage correctness in test execution, and facilitate trade-off analysis of execution states, thereby increasing confidence in QoS assurance and improving test quality.

The mechanisms underlying life machinery are still not completely understood. Something is known, something is “probably” known, other things are still unknown. Scientists all over the world are working very hard to clarify the processes regulating the cell life cycle and bioinformaticians try to support them by developing specialized automated tools. Within the plethora of applications devoted to the study of life mechanisms, tools for the analysis and comparison of biological networks are catching the attention of many researchers. It is interesting to investigate why.

The article is aimed at fingerprint recognition or verification, which is used mainly in access control systems. The proposed method of fingerprint verification uses image registration (reconciliation) by applying the Fourier-Mellin transform and consequently compares images by the Fourier transform using modified phase correlation.

One of the most commonly used predictive models in classification is the decision tree (DT). The task of a DT is to map observations to target values. In the DT, each branch represents a rule. A rule’s consequent is the leaf of the branch and its antecedent is the conjunction of the features. Most applied algorithms in this field use the concept of Information Entropy and Gini Index as the splitting criterion when building a tree. In this paper, a new splitting criterion to build DTs is proposed. A splitting criterion specifies the tree’s best splitting variable as well as the variable’s threshold for further splitting. Using the idea from classical Forward Selection method and its enhanced versions, the variable having the largest absolute correlation with the target value is chosen as the best splitting variable at each node. Then, the idea of maximizing the margin between classes in a support vector machine (SVM) is used to find the best classification threshold on the selected variable. This procedure will execute recursively at each node, until reaching the leaf nodes. The final decision tree has a shorter height than previous methods, which effectively reduces useless variables and the time needed for classification of future data. Unclassified regions are also generated under the proposed method, which can be interpreted as an advantage or disadvantage. The simulation results demonstrate an improvement in the generated decision tree compared to previous methods.

The discovery of association rules is an important and challenging data mining task. Most of the existing algorithms for finding association rules require multiple passes over the entire database, and I/O overhead incurred is extremely high for very large databases. An obvious approach to reduce the complexity of association rule mining is sampling. In recent times, several sampling-based approaches have been developed for speeding up the process of association rule mining. A proficient progressive sampling-based approach is presented for mining association rules from large databases. At first, frequent itemsets are mined from an initial sample and subsequently, the negative border is computed from the mined frequent itemsets. Based on the support computed for the midpoint itemset in the sorted negative border, the sample size is either increased or association rules are mined from it. In this paper, we have presented an extensive analysis of the progressive sampling-based approach with different real life datasets and, in addition, the performance of the approach is evaluated with the well-known association rule mining algorithm, Apriori. The experimental results show that accuracy and computation time of the progressive sampling-based approach is effectively improved in mining of association rules from the real life datasets.

A new VLSI algorithm and its associated systolic array architecture for a prime length type IV discrete cosine transform is presented. They represent the basis of an efficient design approach for deriving a linear systolic array architecture for type IV DCT. The proposed algorithm uses a regular computational structure called pseudoband correlation structure that is appropriate for a VLSI implementation. The proposed algorithm is then mapped onto a linear systolic array with a small number of I/O channels and low I/O bandwidth. The proposed architecture can be unified with that obtained for type IV DST due to a similar kernel. A highly efficient VLSI chip can be thus obtained with good performance in the architectural topology, computing parallelism, processing speed, hardware complexity and I/O costs similar to those obtained for circular correlation and cyclic convolution computational structures.